Double channel CNN for accurate age and gender estimation in complex scenarios

Xiuhong Zhu; Shanjun Li; Guoyou Wang; Lanfang Kong; Zhaoyang Hu

doi:10.1117/12.2503070

9 August 2018 Double channel CNN for accurate age and gender estimation in complex scenarios

Xiuhong Zhu, Shanjun Li, Guoyou Wang, Lanfang Kong, Zhaoyang Hu

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108061O (2018) https://doi.org/10.1117/12.2503070
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

Abstract

In recent years, the application of age and gender estimation from face images are becoming increasingly wider and deeper. Existing age and gender estimation pipelines usually process images through machine learning, like SVM, AdaBoost and etc. However, the performance gain of such method is usually limited to handle images with strict conditions or simple backgrounds. At present, age and gender estimation in an open environment still face enormous challenges. In this paper, we introduce a method based on double channel convolutional neural network (CNN) for accurate age and gender estimation in complex scenarios. To start with, detecting face regions with single-face or multifaces. Secondly, utilizing the face alignment based on the facial landmark detection. Finally, using double channel CNN structure with Xgboost to train the model for age and gender estimation. Experiments show that the proposed method based on double channel CNN can achieve a higher accuracy at comparable time cost compared with single channel CNN method and is robust to face images from wild conditions.

Citation Download Citation

Xiuhong Zhu, Shanjun Li, Guoyou Wang, Lanfang Kong, and Zhaoyang Hu "Double channel CNN for accurate age and gender estimation in complex scenarios", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108061O (9 August 2018); https://doi.org/10.1117/12.2503070

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